The problem of track-to-track association and track fusion has been considered in the literature where the fusion center has access to multiple track estimates and the associated estimation error covariances from local sensors, as well as their cross covariances. Due primarily to the communication constraints in real systems, some legacy trackers may only provide the local track estimates to the fusion center without any covariance information. In some cases, the local (sensor-level) trackers operate with fixed filter gain and do not have any self assessment of their estimation errors. In other cases, the network conveys a coarsely quantized root mean square (RMS) estimation error of each local tracker. Thus the fusion center needs to solve the track association and fusion problem with incomplete data from legacy local trackers. In this paper a robust track-to-track association and fusion algorithm is described for a distributed tracking system, which accounts for the cross correlation of the estimation error between local tracks in a practical way. Its applicability to real-time and different rate data sources is also discussed by generalizing the algorithms from the existing literature to the case of asynchronous sensors. The problem of track fusion with legacy track sources which lack covariance information is handled by approximating this information through a modified Lyapunov equation. The situation when a coarsely quantized RMS estimation error is available is also discussed. A two-sensor tracking example is used to illustrate the effectiveness of the proposed distributed track fusion algorithm and compared with a centralized interacting multiple model estimator